It's worth noting that neither of those books contain any code at all.
I suppose that's what makes the ISLA being translated such a big deal. A sufficiently advanced student in ML/Statistical modeling doesn't really need code at all since it should be fairly trivial to translate the mathematical models into computational ones, and the ability to do so is a prerequisite to understanding these models in the first place.
Recommended Textbooks:
Pattern Recognition and Machine Learning, Christopher Bishop
Machine Learning: A probabilistic perspective, Kevin Murphy
[2] University of Toronto CSC 311: Introduction to Machine Learning
Suggested readings are optional; they are resources we recommend to help you understand the course material. All of the textbooks listed below are freely available online.
Bishop = Pattern Recognition and Machine Learning, by Chris Bishop
ESL = The Elements of Statistical Learning, by Hastie, Tibshirani, and Friedman.
[3] EPFL CS-433 Machine Learning:
Textbooks(not mandatory)
Gilbert Strang, Linear Algebra and Learning from Data
Christopher Bishop, Pattern Recognition and Machine Learning
[4] University of Washington CSE 446: Machine Learning
The required textbook for the course is:
[Murphy] Machine Learning: A Probabilistic Perspective, Kevin Murphy.
The following three texts are also excellent and their PDFs are available for free online.
[B] Pattern Recognition and Machine Learning, Christopher Bishop.
[HTF] The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Trevor Hastie, Robert Tibshirani, Jerome Friedman.
[5] Cornell University ECE4950: Machine Learning and Pattern Recognition
Materials
We will take materials from various sources. Some books are:
Pattern Recognition and Machine Learning, Christopher Bishop
Machine Learning: a Probabilistic Perspective, Kevin Murphy
[6] Princeton University COS 324: Introduction to Machine Learning
Optional Machine Learning Books
[Murphy] Kevin Murphy, Machine Learning: A Probabilistic Perspective, MIT Press.
[Bishop] Christopher M. Bishop, Pattern Recognition and Machine Learning, Springer.
[7] ETH Zurich Introduction to Machine Learning (2023)
Other Resources
K. Murphy. Machine Learning: a Probabilistic Perspective. MIT Press, 2012.
C. Bishop. Pattern Recognition and Machine Learning. Springer, 2007.
[8] TUM (Technical University of Munich) Machine Learning
This award-winning introductory Machine Learning lecture teaches the foundations of and concepts behind a wide range of common machine learning models.
Literature
Pattern Recognition and Machine Learning. Christopher Bishop. Springer-Verlag New York. 2006.
Machine Learning: A Probabilistic Perspective. Kevin Murphy. MIT Press. 2012
[9] MIT Introduction To Machine Learning:
Books: No textbook is required for this class, but students may find it helpful to purchase one of the following books. Bishop's book is much easier to read, whereas Murphy's book has substantially more depth and coverage (and is up to date).
Machine Learning: a Probabilistic Perspective, by Kevin Murphy (2012).
Pattern Recognition and Machine Learning, by Chris Bishop (2006).
[10] UC Berkeley CS-194-10: Introduction to Machine Learning:
Reading List (Preliminary Draft)
The first two books are very helpful, and are available online, so those (in addition to AIMA) will be the primary sources. Bishop has a wide range of solid mathematical derivations, while Witten and Frank focus much more on the practical side of applied machine learning and on the Weka package (a Java library and interface for machine learning).
Trevor Hastie, Rob Tibshirani, and Jerry Friedman, Elements of Statistical Learning, Second Edition, Springer, 2009. (Full pdf available for download.)
Kevin P. Murphy, Machine Learning: A Probabilistic Perspective. Unpublished. Access information will be provided.
Stuart Russell and Peter Norvig, Artificial Intelligence: A Modern Approach, Third Edition, Prentice Hall, 2010.
Christopher Bishop, Pattern Recognition and Machine Learning, Springer, 2006.
Ian Witten and Eibe Frank, Data Mining: Practical Machine Learning Tools and Techniques, Third Edition, Morgan Kaufmann, 2011.
ISL is a more introductory book than Bishop or Murphy. There's no reason not to read all of them, they're all excellent books that cover different topics. I'd also throw in Elements of Statistical Learning from the same authors as ISL(R/P). I've read ISL, ESL, and Bishop, started Murphy but didn't finish it (no real reason, just lost track of it when I got busy). I highly recommend any and all of these texts.
I heard good things about Bishop however I am a SE that would like do know more about what the ML team is doing and maybe work on some ML side projects. Would you recommend Bishop here or is it considerer to theoretical for such a case?
Bishop is going to be more theoretical than ISL. It is true that Bishop is taught as an introduction to ML in many universities, but if you want more hands on to start with, ISL is an excellent option. There is another text called "Elements of Statistical Learning" that pairs well with ISL for a more theoretical treatment. I haven't looked at ESL in a long time, the only concern I'd have is if they aren't covering some introductory deep learning topics. Most of ISL, ESL, and Bishop are more traditional machine learning, covering a wide variety of algorithms, so bear that in mind.
